Analyzing Dynamic Networks

  • N. N. R. Ranga SuriEmail author
  • Narasimha Murty M
  • G. Athithan
Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)


This chapter highlights the importance of analyzing dynamic networks and gives details of various applications requiring this capability. It also furnishes a few recent algorithmic approaches for analyzing such networks in a pragmatic manner.


  1. 1.
    Aggarwal, C., Li, N.: On node classification in dynamic content-based networks. In: SDM, pp. 355–366 (2011)CrossRefGoogle Scholar
  2. 2.
    Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Backstrom, L., Huttenlocher, D.P., Klienberg, J.M., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD, pp. 44–54 (2006)Google Scholar
  4. 4.
    Breunig, M., Kriegel, H., Ng, R., Sander, J.: Lof: identifying density-based local outliers. In: ACM SIGMOD International Conference on Management of Data, Dallas, Texas, pp. 93–104 (2000)Google Scholar
  5. 5.
    Cao, L., Wei, M., Yang, D., Rundensteiner, E.A.: Online outlier exploration over large datasets. In: KDD. ACM (2015)Google Scholar
  6. 6.
    Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: KDD, pp. 554–560 (2006)Google Scholar
  7. 7.
    Fox, C., Roberts, S.: A tutorial on variational Bayesian inference. Artif. Intell. Rev., 85–95 (2012)CrossRefGoogle Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gupta, M., Aggarwal, C., Han, J., Sun, Y.: Evolutionary clustering and analysis of bibliographic networks. In: ASONAM, pp. 63–70 (2011)Google Scholar
  10. 10.
    Gupta, M., Gao, J., Sun, Y., Han, J.: Integrating community matching and outlier detection for mining evolutionary community outliers. In: KDD, pp. 859–867 (2012)Google Scholar
  11. 11.
    Gupte, P.V., Ravindran, B., Parthasarathy, S.: Role discovery in graphs using global features: algorithms, applications and a novel evaluation strategy. In: 33rd International Conference on Data Engineering (ICDE), pp. 771–782. IEEE Computer Society, San Diego, CA, USA (2017)Google Scholar
  12. 12.
    Hellmann, T., Staudigl, M.: Evolution of social networks. Eur. J. Oper. Res. 234, 583–596 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Henderson, K., Eliassi-Rad, T., Faloutsos, C., Akoglu, L., Li, L., Maruhashi, K., Prakash, B.A., Tong, H.: Metric forensics: a multi-level approach for mining volatile graphs. In: KDD, pp. 163–172. ACM (2010)Google Scholar
  14. 14.
    Henderson, K., Gallagher, B., Eliassi-Rad, T., Tong, H., Basu, S., Akoglu, L., Koutra, D., Faloutsos, C., Li, L.: Rox: structural role extraction and mining in large graphs. In: KDD. ACM (2012)Google Scholar
  15. 15.
    Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., Faloutsos, C.: It’s who you know: graph mining using recursive structural features. In: KDD, pp. 1–10. ACM (2011)Google Scholar
  16. 16.
    Lamba, H., Hooi, B., Shin, K., Faloutsos, C., Pfeffer, J.: zooRANK: ranking suspicious entities in time-evolving tensors. In: ECML-PKDD. LNCS, pp. 68–84. Springer, Skopje, Macedonia (2017)CrossRefGoogle Scholar
  17. 17.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature (1999)Google Scholar
  18. 18.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: KDD, pp. 462–470 (2008)Google Scholar
  19. 19.
    Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: KDD, pp. 177–187 (2005)Google Scholar
  20. 20.
    Li, K., Guo, S., Du, N., Gao, J., Zhang, A.: Learning, analyzing and predicting object roles on dynamic networks. In: ICDM, pp. 428–437. IEEE (2013)Google Scholar
  21. 21.
    Morgan, G.P., Frankerstein, W., Carley, K.M.: Introduction to dynamic network analysis. In: BRiMS (2015)Google Scholar
  22. 22.
    Palla, G., Barabasi, A., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  23. 23.
    Rossi, R., Neville, J., Gallagher, B., Henderson, K.: Role-dynamics: fast mining of large dynamic networks. In: WWW, pp. 997–1006. ACM (2012)Google Scholar
  24. 24.
    Sakazume, C., Kitagawa, H., Amagasa, T.: DIO: efficient interactive outlier analysis over dynamic datasets. In: Twelfth International Conference on Digital Information Management (ICDIC), pp. 228–235. IEEE, Fukuoka, Japan (2017)Google Scholar
  25. 25.
    Spiliopoulou, M.: Evolution in social networks: a survey. In: Social Network Data Analytics, pp. 149–175. Springer (2011)Google Scholar
  26. 26.
    Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: KDD, pp. 717–726. ACM (2007)Google Scholar
  27. 27.
    Wang, A., Hamilton, W.L., Leskovec, J.: Learning linguistic descriptors of user roles in online communities. In: Proceedings of the First Workshop on NLP and Computational Social Science, Austin, TX, USA, pp. 76–85 (2016)Google Scholar
  28. 28.
    Wasserman, S.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
    Email author
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

Personalised recommendations